Run DE analysis of a selection of normalized data sets

Usage

run_DE(
  se,
  comparisons,
  ain = NULL,
  condition = NULL,
  DE_method = "limma",
  covariate = NULL,
  logFC = TRUE,
  logFC_up = 1,
  logFC_down = -1,
  p_adj = TRUE,
  alpha = 0.05,
  B = 100,
  K = 500,
  trend = TRUE,
  robust = TRUE,
  DEqMS_PSMs_column = NULL
)

Arguments

se: SummarizedExperiment containing all necessary information of the proteomics data set
comparisons: Vector of comparisons that are performed in the DE analysis (from specify_comparisons method)
ain: Vector of strings which assay should be used as input (default NULL). If NULL then all normalization of the se object are plotted next to each other.
condition: column name of condition (if NULL, condition saved in SummarizedExperiment will be taken)
DE_method: String specifying which DE method should be applied (limma, ROTS, DEqMS)
covariate: String specifying which column to include as covariate into limma
logFC: Boolean specifying whether to apply a logFC threshold (TRUE) or not (FALSE)
logFC_up: Upper log2 fold change threshold (dividing into up regulated)
logFC_down: Lower log2 fold change threshold (dividing into down regulated)
p_adj: Boolean specifying whether to apply a threshold on adjusted p-values (TRUE) or on raw p-values (FALSE)
alpha: Threshold for adjusted p-values or p-values
B: Number of bootstrapping for ROTS
K: Number of top-ranked features for reproducibility optimization
trend: logical, should an intensity-dependent trend be allowed for the prior variance? If FALSE then the prior variance is constant. Alternatively, trend can be a row-wise numeric vector, which will be used as the covariate for the prior variance.
robust: logical, should the estimation of df.prior and var.prior be robustified against outlier sample variances?
DEqMS_PSMs_column: String specifying which column name to use for DEqMS (default NULL). Any column of the rowData(se) is accepted.

Value

Data table of DE results of selected normalized data sets

Examples

data(tuberculosis_TMT_se)
comparisons <- specify_comparisons(tuberculosis_TMT_se, condition = NULL,
                            sep = NULL, control = NULL)
#> Condition of SummarizedExperiment used!
de_res <- run_DE(tuberculosis_TMT_se, comparisons,
              ain = NULL, condition = NULL, DE_method = "limma",
              logFC = TRUE, logFC_up = 1, logFC_down = -1, p_adj = TRUE,
              alpha = 0.05, B = 100, K = 500, trend = TRUE, robust = TRUE)
#> Condition of SummarizedExperiment used!
#> All assays of the SummarizedExperiment will be used.
#> DE Analysis will not be performed on raw data.
#> Warning: Partial NA coefficients for 114 probe(s)
#> log2: DE analysis completed.
#> Warning: Partial NA coefficients for 114 probe(s)
#> RobNorm: DE analysis completed.
#> Warning: Partial NA coefficients for 112 probe(s)
#> IRS_on_RobNorm: DE analysis completed.
#> Warning: Partial NA coefficients for 114 probe(s)
#> Median: DE analysis completed.
#> Warning: Partial NA coefficients for 112 probe(s)
#> IRS_on_Median: DE analysis completed.